Vulnerability is the capacity to anticipate, cope with, resist and recover from the impact of natural disasters. Floods add to the distressed conditions of the poor and vulnerable people in Bihar. Floods have a different impact on households depending on differences in their livelihood choices. Therefore, in order to identify the variability in vulnerability of affected households, the livelihood vulnerability index (LVI) of Hahn, Riederer and Foster was modified according to the context of the study area. The LVI aims to identify sources and forms of vulnerability that are specific to the context in order to design context-specific resilience measures. However, vulnerability and resilience are not interdependent but discrete entities. The study was conducted in the seven blocks of Bhagalpur district in the state of Bihar. Naugachia was found to be the least vulnerable because of better access to basic amenities and livelihood strategies, whilst Kharik was found to be highly vulnerable in respect to other blocks because of high sensitivity and less adaptive strategy. The study also revealed that better access to resources does not necessarily mean that households are adopting resilience measures because of apathetic or indifferent attitudes.

Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words are the most obvious choice as feature for emotion recognition. In addition to that we have introduced two novel feature sets: polarity of subject, verb and object of the sentences and semantic frames. Experiments concerning the comparison of features revealed that semantic frame feature combined with polarity based feature performs best in emotion classification. Experiments on feature selection over word and semantic frame features have been performed in order to handle feature sparseness problem. In both word and semantic frame feature, improvements in the overall performance have been observed after optimal feature selection.

Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work.

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Buchanania lanzan (Chironji), a member of family Anacardiaceaecontains a hard nut that on decortication yields kernel containing about 52 per cent oil and used as a substitute for olive and almond oil while the whole kernel is used in sweet-meals. Although, the chironji nuts and kernels have been used extensively but the printed literature on their physical and engineering properties is scarce. In the present study, attempt has been made to generate primary data on physical and engineering properties which could be used for developing processing machinery(s). The initial moisture content of chironji nuts and kernels was found to vary from 6.60 per cent to 11.07 per cent and from 2.77 per cent to 2.99 per cent (db), respectively. The mean length, width and thickness of chironji nuts were found to be 10.19, 9.12 and 7.32 mm, respectively while corresponding parameters for chironji kernels were 6.80, 5.01 and 4.66 mm. The sphericity and roundness of chironji nuts were found to be 81.85 per cent and 79.45 per cent, respectively while for kernel were 77.08 per cent and 76.41 per cent. The average chironji nut mass was 0.33 gand kernel 0.07 g.

An affective text may be judged to belong to multiple affect categories as it may evoke different affects with varying degree of intensity. For affect classification of text, it is often required to annotate text corpus with affect categories. This task is often performed by a number of human judges. This paper presents a new agreement measure inspired by Kappa coefficient to compute inter-annotator reliability when the annotators have freedom to categorize a text into more than one class. The extended reliability coefficient has been applied to measure the quality of an affective text corpus. An analysis of the factors that influence corpus quality has been provided.

Abstract. Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using MultiLabel K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.